Haizi Yu
ABSTRACT
Ranking users in social networks is a well-studied problem, typically solved by algorithms that leverage network structure to identify influential users and recommend people to follow. In the last decade, however, curation --- users sharing and promoting content in a network --- has become a central social activity, as platforms like Facebook, Twitter, Pinterest, and GitHub drive growth and engagement by connecting users through content and content to users. While existing algorithms reward users that are highly active with higher rankings, they fail to account for users' curatorial taste. This paper introduces CuRank, an algorithm for ranking users and content in social networks by explicitly modeling three characteristics of a good curator: discerning taste, high activity, and timeliness. We evaluate CuRank on datasets from two popular social networks --- GitHub and Vine --- and demonstrate its efficacy at ranking content and identifying good curators.
- 2015. GitHub. (2015). https://github.com/.Google Scholar
- 2015. Vine. (2015). https://vine.co/.Google Scholar
- Shuo Chang, Vikas Kumar, Eric Gilbert, and Loren G. Terveen. 2014a. Specialization, homophily, and gender in a social curation site: findings from Pinterest. In Proc. CSCW. 674--686. Google ScholarDigital Library
- Yi Chang, Lei Tang, Yoshiyuki Inagaki, and Yan Liu. 2014b. What is Tumblr: a statistical overview and comparison. ACM SIGKDD Explorations Newsletter 16, 1 (2014), 21--29. Google ScholarDigital Library
- Jordan Crook. 2015. Spotify introduces 'Found Them First' so you know how cool you are. (2015). http://techcrunch.com/2015/09/09/spotify-introducesfound-them-first-so-you-know-how-cool-you-are/.Google Scholar
- Laura Dabbish, Colleen Stuart, Jason Tsay, and Jim Herbsleb. 2012. Social coding in GitHub: transparency and collaboration in an open software repository. In Proc. CSCW. 1277--1286. Google ScholarDigital Library
- Ying Ding, Erjia Yan, Arthur Frazho, and James Caverlee. 2009. PageRank for ranking authors in co-citation networks. Journal of the American Society for Information Science and Technology 60, 11 (2009), 2229--2243. Google ScholarDigital Library
- Chris Dixon. 2015. Come for the tool, stay for the network. (2015). http://cdixon.org/2015/01/31/come-for-the-tool-stayfor-the-network/.Google Scholar
- David Easley and Jon Kleinberg. 2010. Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press. Google ScholarDigital Library
- Eric Gilbert, Saeideh Bakhshi, Shuo Chang, and Loren Terveen. 2013. I need to try this: a statistical overview of Pinterest. In Proc. CHI. 2427--2436. Google ScholarDigital Library
- Gene H. Golub and Charles F. Van Loan. 1996. Matrix Computations (3rd ed.). Johns Hopkins University Press. Google ScholarDigital Library
- Pankaj Gupta, Ashish Goel, Jimmy Lin, Aneesh Sharma, Dong Wang, and Reza Zadeh. 2013. WTF: the Who to Follow service at Twitter. In Proc. WWW. 505--514. Google ScholarDigital Library
- Catherine Hall and Michael Zarro. 2012. Social curation on the website Pinterest.com. Proc. ASIS&T 49, 1 (2012), 1--9.Google Scholar
- Kyungsik Han, Jin Yea Jang, and Dongwon Lee. 2015. Exploring tag-based like networks. In Extended Abstracts of CHI. 1941--1946. Google ScholarDigital Library
- Yuchen Jing, Lifang Wu, Xiuzhen Zhang, Dan Wang, and Changwen Chen. 2015. Recommending users to follow based on user taste homophily for content curation social networks. In Proc. SRS.Google Scholar
- M. G. Kendall. 1938. A New Measure of Rank Correlation. Biometrika 30, 1/2 (1938), 81--93.Google ScholarCross Ref
- Jon M. Kleinberg. 1999. Authoritative sources in a hyperlinked environment. J. ACM 46, 5 (1999), 604--632. Google ScholarDigital Library
- Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010. What is Twitter, a social network or a news media. In Proc. WWW. 591--600. Google ScholarDigital Library
- Ronny Lempel and Shlomo Moran. 2000. The stochastic approach for link-structure analysis (SALSA) and the TKC e--ect. Computer Networks 33, 1 (2000), 387--401. Google ScholarDigital Library
- Jure Leskovec and Christos Faloutsos. 2006. Sampling from large graphs. In Proc. KDD. 631--636. Google ScholarDigital Library
- Ari Levy. 2011. LinkedIn acquires Pulse mobile-news reading tool for $90 million. (2011). http://www.bloomberg.com/news/articles/2013-04--11/ linkedin-acquires-pulse-mobile-news-reading-tool-for90-million.Google Scholar
- Rinkesh Nagmoti, Ankur Teredesai, and Martine De Cock. 2010. Ranking approaches for microblog search. In Proc. WI-IAT. 153--157. Google ScholarDigital Library
- Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The PageRank citation ranking: bringing order to the Web. Technical Report. Stanford InfoLab.Google Scholar
- Chris Sacca. 2015. What Twitter can be. (2015). http://lowercasecapital.com/2015/06/03/what-twittercan-be-2/.Google Scholar
- Yizhou Sun, Yintao Yu, and Jiawei Han. 2009. Ranking-based clustering of heterogeneous information networks with star network schema. In Proc. KDD. 797--806. Google ScholarDigital Library
- Twitter. 2015. Twitter top 100 most followers. (2015). http://twittercounter.com/pages/100.Google Scholar
- Jianshu Weng, Ee-Peng Lim, Jing Jiang, and Qi He. 2010. Twitterrank: finding topic-sensitive influential twitterers. In Proc. WSDM. 261--270. Google ScholarDigital Library
- Ding Zhou, Sergey Orshanskiy, Hongyuan Zha, and C Lee Giles. 2007. Co-ranking authors and documents in a heterogeneous network. In Proc. ICDM. 739--744. Google ScholarDigital Library
Index Terms
- Accounting for Taste: Ranking Curators and Content in Social Networks
Recommendations
SNDocRank: document ranking based on social networks
WWW '10: Proceedings of the 19th international conference on World wide webTo improve the search results for socially-connect users, we propose a ranking framework, Social Network Document Rank (SNDocRank). This framework considers both document contents and the similarity between a searcher and document owners in a social ...
Social network document ranking
JCDL '10: Proceedings of the 10th annual joint conference on Digital librariesIn search engines, ranking algorithms measure the importance and relevance of documents mainly based on the contents and relationships between documents. User attributes are usually not considered in ranking. This user-neutral approach, however, may not ...
On personalizing Web search using social network analysis
Most of the existing Web search solutions are built for satisfying broad set of users regardless whether naïve or professionals. Further, with the emergence of high speed internet applications and advanced Web 2.0 based Rich Internet Applications (i.e. ...
Comments